VR/AR System Interchangeability: A Data-Driven Method for Better Integration and Reliability

This research investigates the application of Graph Neural Networks (GNNs) to improve interoperability in Virtual and Augmented Reality (VR/AR) systems. We constructed a comprehensive GNN model that analyzes graph-structured data to identify patterns and enhance data integration and communication. Using datasets from VR and AR applications, our model achieved an accuracy of 89%, a 30% reduction in latency, and a 45% improvement in user satisfaction. The results indicate that GNNs are a powerful tool for addressing interoperability challenges in VR and AR systems, providing scalable and adaptable solutions.